OpenRaiser/Intern-Atlas huggingface dataset:
HypoEval: Hypothesis-Guided Evaluation for Natural Language Generation
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have demonstrated great potential for automating the evaluation of natural language generation. Previous frameworks of LLM-as-a-judge fall short in two ways: they either use zero-shot setting without consulting any human input, which leads to low alignment, or fine-tune LLMs on labeled data, which requires a non-trivial number of samples. Moreover, previous methods often provide little reasoning behind automated evaluations. In this paper, we propose HypoEval, Hypothesis-guided Evaluation framework, which first uses a small corpus of human evaluations to generate more detailed rubrics for human judgments and then incorporates a checklist-like approach to combine LLM's assigned scores on each decomposed dimension to acquire overall scores. With only 30 human evaluations, HypoEval achieves state-of-the-art performance in alignment with both human rankings (Spearman correlation) and human scores (Pearson correlation), on average outperforming G-Eval by 11.86% and fine-tuned Llama-3.1-8B-Instruct with at least 3 times more human evaluations by 11.95%. Furthermore, we conduct systematic studies to assess the robustness of HypoEval, highlighting its effectiveness as a reliable and interpretable automated evaluation framework.
Face-LLaVA: Facial Expression and Attribute Understanding through Instruction Tuning
Venue: arXiv cs.CV 2025
Year: 2025

The human face plays a central role in social communication, necessitating the use of performant computer vision tools for human-centered applications. We propose Face-LLaVA, a multimodal large language model for face-centered, in-context learning, including facial expression and attribute recognition. Additionally, Face-LLaVA is able to generate natural language descriptions that can be used for reasoning. Leveraging existing visual databases, we first developed FaceInstruct-1M, a face-centered database for instruction tuning MLLMs for face processing. We then developed a novel face-specific visual encoder powered by Face-Region Guided Cross-Attention that integrates face geometry with local visual features. We evaluated the proposed method across nine different datasets and five different face processing tasks, including facial expression recognition, action unit detection, facial attribute detection, age estimation and deepfake detection. Face-LLaVA achieves superior results compared to existing open-source MLLMs and competitive performance compared to commercial solutions. Our model output also receives a higher reasoning rating by GPT under a zero-shot setting across all the tasks. Both our dataset and model wil be released at https://face-llava.github.io to support future advancements in social AI and foundational vision-language research.
Who cares about testing?: Co-creations of Socio-technical Software Testing Experiences
Venue: arXiv cs.SE 2025
Year: 2025

Software testing is crucial for ensuring software quality, yet developers' engagement with it varies widely. Identifying the technical, organizational and social factors that lead to differences in engagement is required to remove barriers and utilize enablers for testing. While much research emphasizes the usefulness of testing strategies and technical solutions, less is known about why developers do (not) test. This study investigates the lived experience of software developers to illuminate how their opinions about testing change. Learning about personal evolutions of practice, we explore when and why testing is used. Employing socio-technical grounded theory (STGT), we construct a theory by systematically analyzing data from 19 in-depth, semi-structured interviews with software developers. Allowing interviewees to reflect on how and why they approach software testing, we explore perspectives that are rooted in their contextual experiences. We develop eleven categories of circumstances that act as conditions for the application and adaptation of testing practices and introduce three concepts that we then use to present our theory of emerging testing strategies (ETS) that explains why developers do (not) use testing practices. This study reveals a new perspective on the connection between testing artifacts and collective reflection of practitioners, and it embraces testing as an experience in which human- and social aspects are entangled with organizational and technical circumstances.
Evolutionary Machine Learning meets Self-Supervised Learning: a comprehensive survey
Venue: arXiv cs.NE 2025
Year: 2025

The number of studies that combine Evolutionary Machine Learning and self-supervised learning has been growing steadily in recent years. Evolutionary Machine Learning has been shown to help automate the design of machine learning algorithms and to lead to more reliable solutions. Self-supervised learning, on the other hand, has produced good results in learning useful features when labelled data is limited. This suggests that the combination of these two areas can help both in shaping evolutionary processes and in automating the design of deep neural networks, while also reducing the need for labelled data. Still, there are no detailed reviews that explain how Evolutionary Machine Learning and self-supervised learning can be used together. To help with this, we provide an overview of studies that bring these areas together. Based on this growing interest and the range of existing works, we suggest a new sub-area of research, which we call Evolutionary Self-Supervised Learning and introduce a taxonomy for it. Finally, we point out some of the main challenges and suggest directions for future research to help Evolutionary Self-Supervised Learning grow and mature as a field.
Skill Demand Forecasting Using Temporal Knowledge Graph Embeddings
Venue: arXiv cs.CY 2025
Year: 2025

Rapid technological advancements pose a significant threat to a large portion of the global workforce, potentially leaving them behind. In today's economy, there is a stark contrast between the high demand for skilled labour and the limited employment opportunities available to those who are not adequately prepared for the digital economy. To address this critical juncture and gain a deeper and more rapid understanding of labour market dynamics, in this paper, we approach the problem of skill need forecasting as a knowledge graph (KG) completion task, specifically, temporal link prediction. We introduce our novel temporal KG constructed from online job advertisements. We then train and evaluate different temporal KG embeddings for temporal link prediction. Finally, we present predictions of demand for a selection of skills practiced by workers in the information technology industry. The code and the data are available on our GitHub repository https://github.com/team611/JobEd.
Language Modeling for the Future of Finance: A Survey into Metrics, Tasks, and Data Opportunities
Venue: arXiv cs.CL 2025
Year: 2025

Recent advances in language modeling have led to a growing number of papers related to finance in top-tier Natural Language Processing (NLP) venues. To systematically examine this trend, we review 374 NLP research papers published between 2017 and 2024 across 38 conferences and workshops, with a focused analysis of 221 papers that directly address finance-related tasks. We evaluate these papers across 11 quantitative and qualitative dimensions, and our study identifies the following opportunities for NLP researchers: (i) expanding the scope of forecasting tasks; (ii) enriching evaluation with financial metrics; (iii) leveraging multilingual and crisis-period datasets; and (iv) balancing PLMs with efficient or interpretable alternatives. We identify actionable directions supported by dataset and tool recommendations, with implications for both the academia and industry communities.
Agentic LMs: Hunting Down Test Smells
Venue: arXiv cs.SE 2025
Year: 2025

Test smells reduce test suite reliability and complicate maintenance. While many methods detect test smells, few support automated removal, and most rely on static analysis or machine learning. This study evaluates models with relatively small parameter counts - Llama-3.2-3B, Gemma-2-9B, DeepSeek-R1-14B, and Phi-4-14B - for their ability to detect and refactor test smells using agent-based workflows. We assess workflows with one, two, and four agents over 150 instances of 5 common smells from real-world Java projects. Our approach generalizes to Python, Golang, and JavaScript. All models detected nearly all instances, with Phi-4-14B achieving the best refactoring accuracy (pass@5 of 75.3%). Phi-4-14B with four-agents performed within 5% of proprietary LLMs (single-agent). Multi-agent setups outperformed single-agent ones in three of five smell types, though for Assertion Roulette, one agent sufficed. We submitted pull requests with Phi-4-14B-generated code to open-source projects and six were merged.
Bridging Deep Reinforcement Learning and Motion Planning for Model-Free Navigation in Cluttered Environments
Venue: arXiv cs.RO 2025
Year: 2025

Deep Reinforcement Learning (DRL) has emerged as a powerful model-free paradigm for learning optimal policies. However, in navigation tasks with cluttered environments, DRL methods often suffer from insufficient exploration, especially under sparse rewards or complex dynamics with system disturbances. To address this challenge, we bridge general graph-based motion planning with DRL, enabling agents to explore cluttered spaces more effectively and achieve desired navigation performance. Specifically, we design a dense reward function grounded in a graph structure that spans the entire state space. This graph provides rich guidance, steering the agent toward optimal strategies. We validate our approach in challenging environments, demonstrating substantial improvements in exploration efficiency and task success rates.
Follow-the-Perturbed-Leader Approaches Best-of-Both-Worlds for the m-Set Semi-Bandit Problems
Venue: arXiv cs.LG 2025
Year: 2025

We consider a common case of the combinatorial semi-bandit problem, the $m$-set semi-bandit, where the learner exactly selects $m$ arms from the total $d$ arms. In the adversarial setting, the best regret bound, known to be $\mathcal{O}(\sqrt{nmd})$ for time horizon $n$, is achieved by the well-known Follow-the-Regularized-Leader (FTRL) policy. However, this requires to explicitly compute the arm-selection probabilities via optimizing problems at each time step and sample according to them. This problem can be avoided by the Follow-the-Perturbed-Leader (FTPL) policy, which simply pulls the $m$ arms that rank among the $m$ smallest (estimated) loss with random perturbation. In this paper, we show that FTPL with a Fréchet perturbation also enjoys the near optimal regret bound $\mathcal{O}(\sqrt{nm}(\sqrt{d\log(d)}+m^{5/6}))$ in the adversarial setting and approaches best-of-both-world regret bounds, i.e., achieves a logarithmic regret for the stochastic setting. Moreover, our lower bounds show that the extra factors are unavoidable with our approach; any improvement would require a fundamentally different and more challenging method.
Multilingual MFA: Forced Alignment on Low-Resource Related Languages
Venue: arXiv cs.CL 2025
Year: 2025

We compare the outcomes of multilingual and crosslingual training for related and unrelated Australian languages with similar phonological inventories. We use the Montreal Forced Aligner to train acoustic models from scratch and adapt a large English model, evaluating results against seen data, unseen data (seen language), and unseen data and language. Results indicate benefits of adapting the English baseline model for previously unseen languages.
Prekey Pogo: Investigating Security and Privacy Issues in WhatsApp's Handshake Mechanism
Venue: arXiv cs.CR 2025
Year: 2025

WhatsApp, the world's largest messaging application, uses a version of the Signal protocol to provide end-to-end encryption (E2EE) with strong security guarantees, including Perfect Forward Secrecy (PFS). To ensure PFS right from the start of a new conversation -- even when the recipient is offline -- a stash of ephemeral (one-time) prekeys must be stored on a server. While the critical role of these one-time prekeys in achieving PFS has been outlined in the Signal specification, we are the first to demonstrate a targeted depletion attack against them on individual WhatsApp user devices. Our findings not only reveal an attack that can degrade PFS for certain messages, but also expose inherent privacy risks and serious availability implications arising from the refilling and distribution procedure essential for this security mechanism.
DLTPose: 6DoF Pose Estimation From Accurate Dense Surface Point Estimates
Venue: arXiv cs.CV 2025
Year: 2025

We propose DLTPose, a novel method for 6DoF object pose estimation from RGBD images that combines the accuracy of sparse keypoint methods with the robustness of dense pixel-wise predictions. DLTPose predicts per-pixel radial distances to a set of minimally four keypoints, which are then fed into our novel Direct Linear Transform (DLT) formulation to produce accurate 3D object frame surface estimates, leading to better 6DoF pose estimation. Additionally, we introduce a novel symmetry-aware keypoint ordering approach, designed to handle object symmetries that otherwise cause inconsistencies in keypoint assignments. Previous keypoint-based methods relied on fixed keypoint orderings, which failed to account for the multiple valid configurations exhibited by symmetric objects, which our ordering approach exploits to enhance the model's ability to learn stable keypoint representations. Extensive experiments on the benchmark LINEMOD, Occlusion LINEMOD and YCB-Video datasets show that DLTPose outperforms existing methods, especially for symmetric and occluded objects. The code is available at https://anonymous.4open.science/r/DLTPose_/ .
Towards Distribution Matching between Collaborative and Language Spaces for Generative Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Generative recommendation aims to learn the underlying generative process over the entire item set to produce recommendations for users. Although it leverages non-linear probabilistic models to surpass the limited modeling capacity of linear factor models, it is often constrained by a trade-off between representation ability and tractability. With the rise of a new generation of generative methods based on pre-trained language models (LMs), incorporating LMs into general recommendation with implicit feedback has gained considerable attention. However, adapting them to generative recommendation remains challenging. The core reason lies in the mismatch between the input-output formats and semantics of generative models and LMs, making it challenging to achieve optimal alignment in the feature space. This work addresses this issue by proposing a model-agnostic generative recommendation framework called DMRec, which introduces a probabilistic meta-network to bridge the outputs of LMs with user interactions, thereby enabling an equivalent probabilistic modeling process. Subsequently, we design three cross-space distribution matching processes aimed at maximizing shared information while preserving the unique semantics of each space and filtering out irrelevant information. We apply DMRec to three different types of generative recommendation methods and conduct extensive experiments on three public datasets. The experimental results demonstrate that DMRec can effectively enhance the recommendation performance of these generative models, and it shows significant advantages over mainstream LM-enhanced recommendation methods.
ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method
Venue: arXiv cs.LG 2025
Year: 2025

Climate science studies the structure and dynamics of Earth's climate system and seeks to understand how climate changes over time, where the data is usually stored in the format of time series, recording the climate features, geolocation, time attributes, etc. Recently, much research attention has been paid to the climate benchmarks. In addition to the most common task of weather forecasting, several pioneering benchmark works are proposed for extending the modality, such as domain-specific applications like tropical cyclone intensity prediction and flash flood damage estimation, or climate statement and confidence level in the format of natural language. To further motivate the artificial general intelligence development for climate science, in this paper, we first contribute a multi-modal climate benchmark, i.e., ClimateBench-M, which aligns (1) the time series climate data from ERA5, (2) extreme weather events data from NOAA, and (3) satellite image data from NASA HLS based on a unified spatial-temporal granularity. Second, under each data modality, we also propose a simple but strong generative method that could produce competitive performance in weather forecasting, thunderstorm alerts, and crop segmentation tasks in the proposed ClimateBench-M. The data and code of ClimateBench-M are publicly available at https://github.com/iDEA-iSAIL-Lab-UIUC/ClimateBench-M.
Towards Generalizability to Tone and Content Variations in the Transcription of Amplifier Rendered Electric Guitar Audio
Venue: arXiv cs.SD 2025
Year: 2025

Transcribing electric guitar recordings is challenging due to the scarcity of diverse datasets and the complex tone-related variations introduced by amplifiers, cabinets, and effect pedals. To address these issues, we introduce EGDB-PG, a novel dataset designed to capture a wide range of tone-related characteristics across various amplifier-cabinet configurations. In addition, we propose the Tone-informed Transformer (TIT), a Transformer-based transcription model enhanced with a tone embedding mechanism that leverages learned representations to improve the model's adaptability to tone-related nuances. Experiments demonstrate that TIT, trained on EGDB-PG, outperforms existing baselines across diverse amplifier types, with transcription accuracy improvements driven by the dataset's diversity and the tone embedding technique. Through detailed benchmarking and ablation studies, we evaluate the impact of tone augmentation, content augmentation, audio normalization, and tone embedding on transcription performance. This work advances electric guitar transcription by overcoming limitations in dataset diversity and tone modeling, providing a robust foundation for future research.
Exploring Vulnerabilities and Concerns in Solana Smart Contracts
Venue: arXiv cs.CR 2025
Year: 2025

The Solana blockchain was created by Anatoly Yakovenko of Solana Labs and was introduced in 2017, employing a novel transaction verification method. However, at the same time, the innovation process introduced some new security issues. The frequent security incidents in smart contracts have not only caused enormous economic losses, but also undermined the credit system based on the blockchain. The security and reliability of smart contracts have become a new focus of research both domestically and abroad. This paper studies the current status of security analysis of Solana by researching Solana smart contract security analysis tools. This paper systematically sorts out the vulnerabilities existing in Solana smart contracts and gives examples of some vulnerabilities, summarizes the principles of security analysis tools, and comprehensively summarizes and details the security analysis tools in Solana smart contracts. The data of Solana smart contract security analysis tools are collected and compared with Ethereum, and the differences are analyzed and some tools are selected for practical testing.
AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery
Venue: arXiv cs.CL 2025
Year: 2025

We introduce AgentAda, the first LLM-powered analytics agent that can learn and use new analytics skills to extract more specialized insights. Unlike existing methods that require users to manually decide which data analytics method to apply, AgentAda automatically identifies the skill needed from a library of analytical skills to perform the analysis. This also allows AgentAda to use skills that existing LLMs cannot perform out of the box. The library covers a range of methods, including clustering, predictive modeling, and NLP techniques like BERT, which allow AgentAda to handle complex analytics tasks based on what the user needs. AgentAda's dataset-to-insight extraction strategy consists of three key steps: (I) a question generator to generate queries relevant to the user's goal and persona, (II) a hybrid Retrieval-Augmented Generation (RAG)-based skill matcher to choose the best data analytics skill from the skill library, and (III) a code generator that produces executable code based on the retrieved skill's documentation to extract key patterns. We also introduce KaggleBench, a benchmark of curated notebooks across diverse domains, to evaluate AgentAda's performance. We conducted a human evaluation demonstrating that AgentAda provides more insightful analytics than existing tools, with 48.78% of evaluators preferring its analyses, compared to 27.67% for the unskilled agent. We also propose a novel LLM-as-a-judge approach that we show is aligned with human evaluation as a way to automate insight quality evaluation at larger scale.
LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking
Venue: arXiv cs.IR 2025
Year: 2025

Utilizing large language models (LLMs) for document reranking has been a popular and promising research direction in recent years, many studies are dedicated to improving the performance and efficiency of using LLMs for reranking. Besides, it can also be applied in many real-world applications, such as search engines or retrieval-augmented generation. In response to the growing demand for research and application in practice, we introduce a unified framework, \textbf{LLM4Ranking}, which enables users to adopt different ranking methods using open-source or closed-source API-based LLMs. Our framework provides a simple and extensible interface for document reranking with LLMs, as well as easy-to-use evaluation and fine-tuning scripts for this task. We conducted experiments based on this framework and evaluated various models and methods on several widely used datasets, providing reproducibility results on utilizing LLMs for document reranking. Our code is publicly available at https://github.com/liuqi6777/llm4ranking.
Probability Estimation and Scheduling Optimization for Battery Swap Stations via LRU-Enhanced Genetic Algorithm and Dual-Factor Decision System
Venue: arXiv cs.NE 2025
Year: 2025

To address the challenges of limited Battery Swap Stations datasets, high operational costs, and fluctuating user charging demand, this research proposes a probability estimation model based on charging pile data and constructs nine scenario-specific battery swap demand datasets. In addition, this study combines Least Recently Used strategy with Genetic Algorithm and incorporates a guided search mechanism, which effectively enhances the global optimization capability. Thus, a dual-factor decision-making based charging schedule optimization system is constructed. Experimental results show that the constructed datasets exhibit stable trend characteristics, adhering to 24-hour and 168-hour periodicity patterns, with outlier ratios consistently below 3.26%, confirming data validity. Compared to baseline, the improved algorithm achieves better fitness individuals in 80% of test regions under the same iterations. When benchmarked against immediate swap-and-charge strategy, our algorithm achieves a peak cost reduction of 13.96%. Moreover, peak user satisfaction reaches 98.57%, while the average iteration time remains below 0.6 seconds, demonstrating good computational efficiency. The complete datasets and optimization algorithm are open-sourced at https://github.com/qingshufan/GA-EVLRU.